Qiskit Aer

Qiskit is an open-source framework for working with noisy quantum computers at the level of pulses, circuits, and algorithms.

Qiskit is made up of elements that work together to enable quantum computing. This element is Aer, which provides high-performance quantum computing simulators with realistic noise models.

Installation

We encourage installing Qiskit via the pip tool (a python package manager). The following command installs the core Qiskit components, including Aer.

pip install qiskit qiskit-aer

Pip will handle all dependencies automatically for us, and you will always install the latest (and well-tested) version.

To install from source, follow the instructions in the contribution guidelines.

Installing GPU support

In order to install and run the GPU supported simulators on Linux, you need CUDA® 11.2 or newer previously installed. CUDA® itself would require a set of specific GPU drivers. Please follow CUDA® installation procedure in the NVIDIA® web.

If you want to install our GPU supported simulators, you have to install this other package:

pip install qiskit-aer-gpu

The package above is for CUDA&reg 12, so if your system has CUDA® 11 installed, install separate package:

pip install qiskit-aer-gpu-cu11

This will overwrite your current qiskit-aer package installation giving you the same functionality found in the canonical qiskit-aer package, plus the ability to run the GPU supported simulators: statevector, density matrix, and unitary.

Note: This package is only available on x86_64 Linux. For other platforms that have CUDA support, you will have to build from source. You can refer to the contributing guide for instructions on doing this.

Simulating your first quantum program with Qiskit Aer

Now that you have Qiskit Aer installed, you can start simulating quantum circuits with noise. Here is a basic example:

$ python

import qiskit
from qiskit_aer import AerSimulator
from qiskit.providers.fake_provider import FakeManilaV2

# Generate 3-qubit GHZ state
circ = qiskit.QuantumCircuit(3)
circ.h(0)
circ.cx(0, 1)
circ.cx(1, 2)
circ.measure_all()

# Construct an ideal simulator
aersim = AerSimulator()

# Perform an ideal simulation
result_ideal = aersim.run(circ).result()
counts_ideal = result_ideal.get_counts(0)
print('Counts(ideal):', counts_ideal)
# Counts(ideal): {'000': 493, '111': 531}

# Construct a noisy simulator backend from an IBMQ backend
# This simulator backend will be automatically configured
# using the device configuration and noise model
backend = FakeManilaV2()
aersim_backend = AerSimulator.from_backend(backend)

# Perform noisy simulation
result_noise = aersim_backend.run(circ).result()
counts_noise = result_noise.get_counts(0)

print('Counts(noise):', counts_noise)
# Counts(noise): {'101': 16, '110': 48, '100': 7, '001': 31, '010': 7, '000': 464, '011': 15, '111': 436}

Contribution Guidelines

If you'd like to contribute to Qiskit, please take a look at our contribution guidelines. This project adheres to Qiskit's code of conduct. By participating, you are expected to uphold this code.

We use GitHub issues for tracking requests and bugs. Please use our slack for discussion and simple questions. To join our Slack community use the link. For questions that are more suited for a forum, we use the Qiskit tag in the Stack Exchange.

Next Steps

Now you're set up and ready to check out some of the other examples from our Qiskit IQX Tutorials or Qiskit Community Tutorials repositories.

Authors and Citation

Qiskit Aer is the work of many people who contribute to the project at different levels. If you use Qiskit, please cite as per the included BibTeX file.

License

Apache License 2.0